School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China.
School of Future Transportation, University of Chang'an, Xi'an 710064, China.
Sensors (Basel). 2022 Jun 1;22(11):4230. doi: 10.3390/s22114230.
Effective noise reduction and abnormal feature extraction are important for abnormal sound detection occurring in urban traffic operations. However, to improve the detection accuracy of continuous traffic flow and even overlapping vehicle bodies, effective methods capable to achieve accurate signal-to-noise ratio and appropriate characteristic parameters should be explored. In view of the disadvantages of traditional traffic detection methods, such as Short-Time Energy (STE) and Mel Frequency Cepstral Coefficients (MFCC), this study adopts an improved spectral subtraction method to analyze traffic noise. Through the feature fusion of STE and MFCC coefficients, an innovative feature parameter, E-MFCC, is obtained, assisting to propose a traffic noise detection solution based on Triangular Wave Analysis (TWA). APP Designer in MATLAB was used to establish a traffic detection simulation platform. The experimental results showed that compared with the accuracies of traffic detection using the traditional STE and MFCC methods as 67.77% and 76.01%, respectively, the detection accuracy of the proposed TWA is significantly improved, attaining 91%. The results demonstrated the effectiveness of the traffic detection method proposed in solving the overlapping problem, thus achieving accurate detection of road traffic volume and improving the efficiency of road operation.
有效降低噪声和提取异常特征对于检测城市交通运行中出现的异常声音非常重要。然而，为了提高连续交通流甚至重叠车辆的检测精度，需要探索能够实现精确信噪比和适当特征参数的有效方法。鉴于传统交通检测方法（如短时能量（STE）和梅尔频率倒谱系数（MFCC））的缺点，本研究采用改进的谱减法对交通噪声进行分析。通过 STE 和 MFCC 系数的特征融合，获得了一个创新的特征参数 E-MFCC，辅助提出了一种基于三角波分析（TWA）的交通噪声检测解决方案。使用 MATLAB 中的 APP Designer 建立了交通检测仿真平台。实验结果表明，与传统 STE 和 MFCC 方法的交通检测精度分别为 67.77%和 76.01%相比，所提出的 TWA 的检测精度显著提高，达到 91%。结果表明，所提出的交通检测方法在解决重叠问题方面是有效的，从而实现了道路交通量的准确检测，提高了道路运行效率。
